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@the.boys.in.white.madrid: They're both cooking 😭🔥 | #kylianmbappe #ousmanedembele #targetaudience #tbiw #TheBoysInWhite | France vs Morocco | Mbappe goal vs Morocco today | Dembele goal vs Morocco today | World Cup Quarter Finals | World Cup 2026

TheBoysInWhite - TBIW
TheBoysInWhite - TBIW
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Region: XK
Thursday 09 July 2026 22:25:38 GMT
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Comments

yallyannoying_
-🇦🇴 :
The 2 best players in the France team rn according to sofascore
2026-07-10 05:39:34
0
absolutemadrid_15
_cmm9 :
Dang a madrid fan editing messi
2026-07-09 22:46:03
0
maxv3rstappink
gru :
almost another goal from elise though 😭😳
2026-07-09 22:55:50
0
goat_madrid7
🎭 :
Who loves thsi goat 🐐
2026-07-09 22:27:36
7
okaklahr
Okaklahr :
Messi at 0 percent 😂😂
2026-07-09 22:30:49
0
someoneyoudontknow748
idk :
W
2026-07-09 22:26:59
0
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McLaren P1 GTR ❤️
McLaren P1 GTR ❤️
Building a proof-of-concept AI agent can be done in a single script. But moving that agent into production? That requires a robust, scalable architecture. If you are transitioning from basic LLM scripts to engineering production-grade agents, keeping your codebase clean is half the battle. Here is a battle-tested blueprint for an AI Agent Project Structure that keeps your code modular, maintainable, and highly testable: 📂 The Core Breakdown * agent/ (The Brain): Houses your core agent logic, execution loops, state management, and memory handling. Keep this isolated from specific tool implementations. * tools/ (The Capabilities): Where your agent's superpowers live. Separate your search, calculation, or external API calling scripts into standalone, modular tools. * models/ (The Engines): Manages your LLM and embedding client configurations. If you switch from OpenAI to Anthropic or an open-source model, this is the only place you should need to touch. * prompts/ (The Instructions): Never hardcode prompts into your application logic. Dedicate a folder to system and agent prompt templates to make iterating on prompt engineering seamless. * api/ (The Gateway): Wraps your agent into an API layer (using FastAPI, Flask, etc.) so it can easily integrate with frontend apps or external microservices. * tests/, data/, & logs/ (The Production Guardrails): Essential for evaluating agent performance, tracking execution paths, and catching edge cases before they hit production. > 💡 Pro-Tip: Notice the .env file in the root? Always use it to manage your LLM API keys and environment variables, and ensure it is included in your .gitignore so secrets are never pushed to GitHub! > How do you structure your AI projects? Do you prefer a monolithic approach early on, or do you start modular from day one? Let’s discuss in the comments below! 👇 #AIAgents #ArtificialIntelligence #SoftwareEngineering #Python #LLM
Building a proof-of-concept AI agent can be done in a single script. But moving that agent into production? That requires a robust, scalable architecture. If you are transitioning from basic LLM scripts to engineering production-grade agents, keeping your codebase clean is half the battle. Here is a battle-tested blueprint for an AI Agent Project Structure that keeps your code modular, maintainable, and highly testable: 📂 The Core Breakdown * agent/ (The Brain): Houses your core agent logic, execution loops, state management, and memory handling. Keep this isolated from specific tool implementations. * tools/ (The Capabilities): Where your agent's superpowers live. Separate your search, calculation, or external API calling scripts into standalone, modular tools. * models/ (The Engines): Manages your LLM and embedding client configurations. If you switch from OpenAI to Anthropic or an open-source model, this is the only place you should need to touch. * prompts/ (The Instructions): Never hardcode prompts into your application logic. Dedicate a folder to system and agent prompt templates to make iterating on prompt engineering seamless. * api/ (The Gateway): Wraps your agent into an API layer (using FastAPI, Flask, etc.) so it can easily integrate with frontend apps or external microservices. * tests/, data/, & logs/ (The Production Guardrails): Essential for evaluating agent performance, tracking execution paths, and catching edge cases before they hit production. > 💡 Pro-Tip: Notice the .env file in the root? Always use it to manage your LLM API keys and environment variables, and ensure it is included in your .gitignore so secrets are never pushed to GitHub! > How do you structure your AI projects? Do you prefer a monolithic approach early on, or do you start modular from day one? Let’s discuss in the comments below! 👇 #AIAgents #ArtificialIntelligence #SoftwareEngineering #Python #LLM
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